pmlpp/output_layer/output_layer.h

138 lines
4.7 KiB
C++

#ifndef MLPP_OUTPUT_LAYER_H
#define MLPP_OUTPUT_LAYER_H
/*************************************************************************/
/* output_layer.h */
/*************************************************************************/
/* This file is part of: */
/* PMLPP Machine Learning Library */
/* https://github.com/Relintai/pmlpp */
/*************************************************************************/
/* Copyright (c) 2023-present Péter Magyar. */
/* Copyright (c) 2022-2023 Marc Melikyan */
/* */
/* Permission is hereby granted, free of charge, to any person obtaining */
/* a copy of this software and associated documentation files (the */
/* "Software"), to deal in the Software without restriction, including */
/* without limitation the rights to use, copy, modify, merge, publish, */
/* distribute, sublicense, and/or sell copies of the Software, and to */
/* permit persons to whom the Software is furnished to do so, subject to */
/* the following conditions: */
/* */
/* The above copyright notice and this permission notice shall be */
/* included in all copies or substantial portions of the Software. */
/* */
/* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, */
/* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF */
/* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.*/
/* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY */
/* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, */
/* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE */
/* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. */
/*************************************************************************/
#include "core/math/math_defs.h"
#include "core/string/ustring.h"
#include "core/object/reference.h"
#include "../activation/activation.h"
#include "../cost/cost.h"
#include "../regularization/reg.h"
#include "../utilities/utilities.h"
#include "../lin_alg/mlpp_matrix.h"
#include "../lin_alg/mlpp_vector.h"
class MLPPOutputLayer : public Reference {
GDCLASS(MLPPOutputLayer, Reference);
public:
int get_n_hidden();
void set_n_hidden(const int val);
MLPPActivation::ActivationFunction get_activation();
void set_activation(const MLPPActivation::ActivationFunction val);
MLPPCost::CostTypes get_cost();
void set_cost(const MLPPCost::CostTypes val);
Ref<MLPPMatrix> get_input();
void set_input(const Ref<MLPPMatrix> &val);
Ref<MLPPVector> get_weights();
void set_weights(const Ref<MLPPVector> &val);
real_t get_bias();
void set_bias(const real_t val);
Ref<MLPPVector> get_z();
void set_z(const Ref<MLPPVector> &val);
Ref<MLPPVector> get_a();
void set_a(const Ref<MLPPVector> &val);
real_t get_z_test();
void set_z_test(const real_t val);
real_t get_a_test();
void set_a_test(const real_t val);
Ref<MLPPVector> get_delta();
void set_delta(const Ref<MLPPVector> &val);
MLPPReg::RegularizationType get_reg();
void set_reg(const MLPPReg::RegularizationType val);
real_t get_lambda();
void set_lambda(const real_t val);
real_t get_alpha();
void set_alpha(const real_t val);
MLPPUtilities::WeightDistributionType get_weight_init();
void set_weight_init(const MLPPUtilities::WeightDistributionType val);
bool is_initialized();
void initialize();
void forward_pass();
void test(const Ref<MLPPVector> &x);
MLPPOutputLayer(int p_n_hidden, MLPPActivation::ActivationFunction p_activation, MLPPCost::CostTypes p_cost, Ref<MLPPMatrix> p_input, MLPPUtilities::WeightDistributionType p_weight_init, MLPPReg::RegularizationType p_reg, real_t p_lambda, real_t p_alpha);
MLPPOutputLayer();
~MLPPOutputLayer();
protected:
static void _bind_methods();
int _n_hidden;
MLPPActivation::ActivationFunction _activation;
MLPPCost::CostTypes _cost;
Ref<MLPPMatrix> _input;
Ref<MLPPVector> _weights;
real_t _bias;
Ref<MLPPVector> _z;
Ref<MLPPVector> _a;
real_t _z_test;
real_t _a_test;
Ref<MLPPVector> _delta;
// Regularization Params
MLPPReg::RegularizationType _reg;
real_t _lambda; /* Regularization Parameter */
real_t _alpha; /* This is the controlling param for Elastic Net*/
MLPPUtilities::WeightDistributionType _weight_init;
bool _initialized;
};
#endif /* OutputLayer_hpp */